human subjects improved only slowly over time
and failed to beat simple computer models. This
raises questions about how much human expertise
is desirable when building models for complex dynamic environments. The best way to find out is to
compare how well humans and models do in specific domains and perhaps develop hybrid models
that integrate different approaches.

AI systems have been rapidly improving in recent
years. Traditional expert systems used rule-based
models that mimicked human expertise by employing if-then rules (for example, “If symptoms X, Y, and
Z are present, then try solution #5 first.”). 32 Most AI
applications today, however, use network structures,
which search for new linkages between input variables and output results. In deep neural nets used in
AI applications, the aim is to analyze very large data
sets so that the system can discover complex relationships and refine them whenever more feedback is
provided. AI is thriving thanks to deep neural nets
developed for particular tasks, including playing
games like chess and Go, driving cars, synthesizing
speech, and translating language. 33

Companies should be closely tracking the development of AI applications to determine which
aspects are worthiest of adoption and adaptation in
their industry. Bridgewater Associates LP, a hedge
fund firm based in Westport, Connecticut, is an example of a company already experimenting with AI.
Bridgewater Associates is developing various algorithmic models designed to automate much of the
management of the firm by capturing insights from
the best minds in the organization.34

Artificial general intelligence of the kind that
most humans exhibit is emerging more slowly than
targeted AI applications. Artificial general intelligence remains a rather small portion of current
AI research, with the high-commercial-value work
focused on narrow domains such as speech recognition, object classification in photographs, or
handwriting analysis. 35 Still, the idea of artificial
general intelligence has captured the popular imagination, with movies depicting real-life robots
capable of performing a broad range of complex
tasks. In the near term, the best predictive business
systems will likely deploy a complex layering of
humans and machines in order to garner the comparative advantages of each. Unlike machines,

human experts possess general intelligence that is
naturally sensitive to real-world contexts and is capable of deep self-reflection and moral judgments.

5. Change the Way theOrganization Operates

In our view, the most powerful decision-support systems are hybrids that fuse multiple technologies
together. Such decision aids will become increasingly
common, expanding beyond narrow applications
such as sales forecasting to providing a foundation
for broader systems such as IBM’s Watson, which,
among other things, helps doctors make complex
medical diagnoses. Over time, we expect the underlying technologies to become more and more
sophisticated, eventually reaching the point where
decision-support devices will be on par with, or better than, most human advisers.

As machines become more sophisticated, humans and organizations will advance as well. To
eliminate the excessive noise that often undermines
human judgments in many organizations and to
amplify the signals that truly matter, we recommend
two strategies. First, organizations can record people’s judgments in “prediction banks” to monitor
their accuracy over time. 36 Rather than being overly
general, predictions should be clear and crisp so they
can be unambiguously scored ex post (without any
wiggle room). Second, once managers accumulate
personal performance scores in the prediction bank,
their track record can help determine their “
reputa-tional capital” (which might determine how much
weight their view gets in future decisions). Ray Dalio,
founder of Bridgewater Associates, has been moving
in this direction. He has developed a set of rules and
management principles to create a culture that records, scores, and evaluates judgments on an
ongoing basis, with high transparency and incentives for personal improvement. 37

Truly intelligent enterprises will blend the soft
side of human judgment, including its known frail-ties and biases, with the hard side of big data and
business analytics to create competitive advantages
for companies competing in knowledge economies.
From an organizational perspective, the type of
transformation we envision will require focusing on
three factors. The first involves strategic focus. Leaders will need to determine what kind of intelligence